Kooperativer Bibliotheksverbund

Berlin Brandenburg

and
and

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Electrocardiography
Type of Medium
Language
Year
  • 1
    Language: English
    In: IEEE Transactions on Biomedical Engineering, March 2016, Vol.63(3), pp.664-675
    Description: Goal: This paper presents a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system. Methods: An adaptive implementation of 1-D convolutional neural networks (CNNs) is inherently used to fuse the two major blocks of the ECG classification into a single learning body: feature extraction and classification. Therefore, for each patient, an individual and simple CNN will be trained by using relatively small common and patient-specific training data, and thus, such patient-specific feature extraction ability can further improve the classification performance. Since this also negates the necessity to extract hand-crafted manual features, once a dedicated CNN is trained for a particular patient, it can solely be used to classify possibly long ECG data stream in a fast and accurate manner or alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. Results: The results over the MIT-BIH arrhythmia benchmark database demonstrate that the proposed solution achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopic beats. Conclusion: Besides the speed and computational efficiency achieved, once a dedicated CNN is trained for an individual patient, it can solely be used to classify his/her long ECG records such as Holter registers in a fast and accurate manner. Significance: Due to its simple and parameter invariant nature, the proposed system is highly generic, and, thus, applicable to any ECG dataset.
    Keywords: Electrocardiography ; Neurons ; Feature Extraction ; Kernel ; Databases ; Training ; Monitoring ; Patient-Specific ECG Classification ; Convolutional Neural Networks ; Real-Time Heart Monitoring ; Medicine ; Engineering
    ISSN: 0018-9294
    E-ISSN: 1558-2531
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    Language: English
    In: Sci Rep, 2017, Vol.7(1), pp.9270-9270
    Description: Each year more than 7 million people die from cardiac arrhythmias. Yet no robust solution exists today to detect such heart anomalies right at the moment they occur. The purpose of this study was to design a personalized health monitoring system that can detect early occurrences of arrhythmias from an individual’s electrocardiogram (ECG) signal. We first modelled the common causes of arrhythmias in the signal domain as a degradation of normal ECG beats to abnormal beats. Using the degradation models, we performed abnormal beat synthesis which created potential abnormal beats from the average normal beat of the individual. Finally, a Convolutional Neural Network (CNN) was trained using real normal and synthesized abnormal beats. As a personalized classifier, the trained CNN can monitor ECG beats in real time for arrhythmia detection. Over 34 patients’ ECG records with a total of 63,341 ECG beats from the MIT-BIH arrhythmia benchmark database, we have shown that the probability of detecting one or more abnormal ECG beats among the first three occurrences is higher than 99.4% with a very low false-alarm rate.
    Keywords: Biology;
    ISSN: 2045-2322
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    Language: English
    In: 21st European Signal Processing Conference (EUSIPCO 2013), September 2013, pp.1-5
    Description: The rational function systems proved to be useful in several areas including system and control theories and signal processing. In this paper, we present an extension of the well-known particle swarm optimization (PSO) method based on the hyperbolic geometry. We applied this method on digital signals to determine the optimal parameters of the rational function systems. Our goal is to minimize the error between the approximation and the original signal while the poles of the system remain stable. Namely, we show that the presented algorithm is suitable to localize the same poles by using different initial conditions.
    Keywords: Approximation Algorithms ; Vectors ; Approximation Methods ; Electrocardiography ; Particle Swarm Optimization ; Optimization ; Geometry ; Rational Functions ; Malmquist-Takenaka System ; Hyperbolic Geometry ; Particle Swarm Optimization ; Engineering
    ISSN: 2219-5491
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
    Source: IEEE eBooks
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    Language: English
    In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015, Vol.2015, pp.2608-11
    Description: We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).
    Keywords: Algorithms ; Neural Networks (Computer)
    ISBN: 9781424492718
    ISSN: 1557-170X
    ISSN: 1094687X
    E-ISSN: 15584615
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    Language: English
    In: IEEE transactions on bio-medical engineering, May 2009, Vol.56(5), pp.1415-26
    Description: This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feedforward and fully connected artificial neural networks, which are optimally designed for each patient by the proposed multidimensional particle swarm optimization technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Over the entire database, the average accuracy-sensitivity performances of the proposed system for VEB and SVEB detections are 98.3%-84.6% and 97.4%-63.5%, respectively. Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.
    Keywords: Signal Processing, Computer-Assisted ; Arrhythmias, Cardiac -- Physiopathology ; Electrocardiography -- Methods ; Pattern Recognition, Automated -- Methods
    ISSN: 00189294
    E-ISSN: 1558-2531
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    Language: English
    In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010, Vol.2010, pp.4695-8
    Description: In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.
    Keywords: Algorithms ; Cluster Analysis ; Expert Systems ; Arrhythmias, Cardiac -- Diagnosis ; Diagnosis, Computer-Assisted -- Methods ; Electrocardiography, Ambulatory -- Methods ; Pattern Recognition, Automated -- Methods
    ISBN: 9781424441235
    ISSN: 1557-170X
    ISSN: 1094687X
    E-ISSN: 15584615
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 7
    Language: English
    In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 2008, pp.5474-5477
    Description: In this paper, we present an automated patient-specific electrocardiogram (ECG) beat classifier designed for accurate detection of premature ventricular contractions (PVCs). In the proposed feature extraction scheme, the principal component analysis (PCA) is applied to the dyadic wavelet transform (DWT) of the ECG signal to extract morphological ECG features, which are then combined with the temporal features to form a resultant efficient feature vector. For the classification scheme, we selected the feed-forward artificial neural networks (ANNs) optimally designed by the multi-dimensional particle swarm optimization (MD-PSO) technique, which evolves the structure and weights of the network specifically for each patient. Training data for the ANN classifier include both global (total of 150 representative beats randomly sampled from each class in selected training files) and local (the first 5 min of a patient's ECG recording) training patterns. Simulation results using 40 files in the MIT/BIH arrhythmia database achieved high average accuracy of 97% for differentiating normal, PVC, and other beats.
    Keywords: Engineering
    ISBN: 9781424418145
    ISBN: 1424418143
    ISSN: 1557170X
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    Language: English
    In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009, Vol.2009, pp.1883-8
    Description: In this paper we present a personalized long-term electrocardiogram (ECG) classification framework, which can be applied to any Holter register recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is automatically extracted from a time frame of homogeneous (similar) beats. We tested the system on a benchmark database where beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and thus we used exhaustive K-means clustering in order to find out (near-) optimal number of key-beats as well as the master key-beats. The classification process produced results that were consistent with the manual labels with over 99% average accuracy, which basically shows the efficiency and the robustness of the proposed system over massive data (feature) collections in high dimensions.
    Keywords: Electrocardiography -- Methods ; Electrocardiography, Ambulatory -- Methods
    ISBN: 9781424432967
    ISSN: 1557-170X
    ISSN: 1094687X
    E-ISSN: 15584615
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    Language: English
    In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, pp.8360-8364
    Description: 1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the "Big Data" scale in order to prevent the well-known "overfitting" problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction, denoising, etc. Furthermore, due to the simple and compact configuration of such adaptive 1D CNNs that perform only linear 1D convolutions (scalar multiplications and additions), a real-time and low-cost hardware implementation is feasible. This paper reviews the major signal processing applications of compact 1D CNNs with a brief theoretical background. We will present their state-of-the-art performances and conclude with focusing on some major properties. Keywords - 1-D CNNs, Biomedical Signal Processing, SHM
    Keywords: Electrocardiography ; Two Dimensional Displays ; Training ; Vibrations ; Feature Extraction ; Signal Processing ; Monitoring ; Engineering
    E-ISSN: 2379-190X
    Source: IEEE Conference Publications
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines 
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages